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Sampling Cheat Sheet by

Sampling - Random and Non-random


The population is the collection of units (people, objects or whatever) that resear­chers are interested in knowing about. The number of indivi­duals in a population is called population size. Population may be finite (we know the numbers exactly) or infinite (no idea about the number).
A sample is a smaller collection of units selected from the population i.e. a finite subset of indivi­duals in a population is called a sample and the number of indivi­duals in a sample is called sample size.
The terms that describe the charac­ter­istics of a population
the terms describe the charac­ter­istics of sample
Collection of all items under study
Part or portion of population chosen for study
Population size = N
Sample size = n
Mean = μ
Mean = x̄
Standard Deviation = σ
Standard Deviation = S
Correl­ation Coeff. = ρ
Correl­ation Coeff. = r
Data are collected for each and every unit (person, household, shop, organi­zation etc.) of the popula­tion.
Instead of every unit of the popula­tion, only a part of the population is studied and the conclu­sions are drawn on that basis for the entire popula­tion.
Sampling Frame
(Source list) The sampling frame is the list of items in the population (universe) from which sample is to be drawn.

Sampling Process

Define the population
Specify the sampling frame
Specify sampling unit
Selection of sampling method
Determ­ination of sample size
Specify the sampling plan
Select the sample

Sample Design

Technique or the procedure the researcher would adopt in selecting items for the sample. Sample design is determined before data are collected. It must consider:
the sampling frame
technique of selection of sample
sample size

Sampling Techniques

Sampling Techniques
Random (Proba­bility)
Non random (Non probab­ility)
Simple Random Sampling
Judgement Sampling
Stratified Sampling
Snowball Sampling
Systematic Sampling
Conven­ience Sampling
Multistage Sampling
Quota Sampling
Cluster Sampling
Random: Simple Stratified System of Multistage Cluster
Non-Ra­ndom: John Snow Convinces Queen

Simple Random Sampling

Every individual or item from a frame has the same chance of selection as every other individual or item.
n is used to represent the sample size and N is used to represent the frame size.
Every item in the frame is numbered from 1 to N. The chance that any particular member of the frame is selected on the first draw is 1/N.
Random sample can be obtained by any of the following methods:
 ­ Lottery Method
 ­ Random number method
 ­ Random number generator (by different software)

Stratified Random Sampling

used when we have to select samples from a hetero­geneous population such as male and female, or educated and uneduc­ated, etc
the population is divided into subgroups or strata and a simple random sample is taken from each such subgroup.
each stratum is homoge­neous internally and hetero­geneous with other strata.
sampling can be either propor­tionate or dispro­por­tio­nate.
 ­  increases a sample’s statis­tical effici­ency.
 ­  provides adequate data for analyzing the various subpop­ula­tions or strata
 ­  enables different research methods and procedures to be used in different strata.

Systematic Random Sampling

random selection of the first item and then the selection of a sample item at every kth interval.
The interval k is fixed by dividing the population by sample size.
K = Size of population / Size of sample required = N/n

Cluster Sampling

involves dividing the population into non overla­pping areas or clusters.
in contrast to stratified random sampling where strata are homoge­neous, cluster sampling identifies clusters that tend to be internally hetero­gen­eous.
cluster contains a wide variety of elements, and the cluster is a miniature, or microcosm, of the popula­tion. eg. city, homes, colleges, etc.
Often clusters are naturally occurring groups of the population
Two of the foremost advantages are conven­ience and cost.

Multistage Sampling

further develo­pment of the principle of cluster sampling.
consists of first selecting the clusters and then selecting a specified number of elements from each selected cluster is known as sub sampling or two stage sampling.
clusters which form the units of sampling at the first stage are called the first stage units (fsu) or primary sampling units (psu)
the elements within clusters are called second stage units (ssu).

Judgment Sampling

when elements selected for the sample are chosen by the judgment of the researcher
profes­sional resear­chers might believe they can select a more repres­ent­ative sample than the random process will provide
saving time and money
The sampling error cannot be determined object­ively because probab­ilities are based on nonrandom selection.
Example: Market selection for the constr­uction of consumer price index

Snowball Sampling

subjects are selected based on referral from other survey respon­dents.
The researcher identifies a person who fits the profile of subjects wanted for the study. The researcher then asks this person for the names and locations of others who also fit the profile of subjects wanted for the study.
partic­ularly useful when subjects are difficult to locate
survey subjects can be identified cheaply and effici­ently
main disadv­antage is that it is nonrandom

Conven­ience Sampling

elements for the sample are selected for the conven­ience of the researcher
researcher typically chooses elements that are readily available, nearby or willing to partic­ipate.
For example, a conven­ience sample of homes for door to door interviews might include houses people are at home, houses with no dogs, houses near the street, first floor apartments and houses with friendly people.
If a research firm is located in a mall, a conven­ience sample might be selected by interv­iewing only shoppers who pass the shop and look friendly.

Quota Sampling

Certain population subcla­sses, such as age group, gender or geogra­phical region are used as strata.
instead of randomly sampling from each stratum, the researcher uses a nonrandom sampling method to gather data from each stratum until the desired quota of samples is filled.
a quota is based on the propor­tions of the subclasses in the popula­tion.
an interv­iewer would begin by asking a few filter questions; if the respondent represents a subclass whose quota has been filled, the interv­iewer would terminate the interview.


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